The term Information Age was coined sometime in the last couple of decades of the twentieth century.

This was a time characterized by the exponential growth of Information Technology (IT), the Internet, and unbridled enthusiasm and optimism about the beneficence of the IT driven paradigm shift.  

In the twenty first century, after the bust and the financial crisis of 2008, our enthusiasm has been toned down considerably as we realize the limitations of the technology. However, as we survey the landscape in recent years, we find ourselves riding a new wave of technology-driven optimism. It is now the age of Big Data and its technological manifestations, The Cloud and Data Science/Analytics.

Human civilizations, over the centuries, have left a trail of social, cultural, technological and scientific data--so data creation is not new to our species. At the root of the contemporary Big Data age is the enormous volume and diversity of data that is generated and consumed by us in our daily lives.  

Technology frames Big Data by three V words--Volume, Velocity and Variety. The quantity, speed and diversity of data are expanding at a geometric rate, and conventional IT is grossly inadequate for storing and analyzing it in a timely or effective way. By some estimates, every two days, we generate and consume as much data as was produced from the dawn of civilization until 2003! This scale of data/information is unprecedented and new technologies that are geared to this scale are now emerging.

The Cloud refers to storage technology (servers) and it resides in the network/internet. Analytics refers to the collection of algorithms and techniques used to gain insight from the data. Thus, Big Data, Cloud and Analytics form a mutually dependent triumvirate.

Digging deeper

The Recommendation Engine is the most public face of Big Data analytics. We are all intimately familiar with the usefulness of recommendations made by Google, Amazon, Netflix, and others.  Recommendation Engines parse through gargantuan volumes of data to reduce and produce results shaped by the preferences of the human user.  

Another star child of Big Data is predictive analytics--the ability to predict user/consumer behavior based on historical data generated by the consumer. Individual consumers and business professionals have become so reliant on recommendation and prediction engines, that commerce as we know it today would be virtually impossible without these services. It is not surprising, therefore, to find that virtually every new business that uses the cloud provides recommendations and it uses prediction services for activities such as targeted marketing.

For all its beneficent promises, Big Data does have a dark side. The effectiveness of recommendations and predictions is predicated on the sophistication of “learning algorithms” of the relevant engines.  This usually requires obtrusive and unobtrusive collection of personal demographic, social, and behavioral data. It is this feature of Big Data that has raised concerns about violations of privacy and security.  

The security concerns of storing sensitive financial and personal data in the Cloud are readily understood. But the issue of privacy is one that has deep and wide ramifications not just for individuals but also for society at large. Revelations about the extensive surveillance activities of the security apparatus in the US and the UK have brought this issue to the forefront of public debate.

Facebook study

To add fuel to the fire, in 2015 Facebook published details of a massive experiment in which it manipulated information posted on 689,000 users' home pages by filtering users' news feeds. It included the flow of comments, videos, pictures and web links posted by other people in their social network.  

The study concluded: "Emotions expressed by friends, via online social networks, influence our own moods, constituting, to our knowledge, the first experimental evidence for massive-scale emotional contagion via social networks." The insidious consequences of “emotional contagion via social networks” has become painfully obvious in our post-presidential election world of fake news generated by human operators and artificially intelligent software agents or bots.

Future problems

As more and more industries, including higher education, co-opt the online platform into their business models, they will find themselves trying to strike an appropriate balance between leveraging the potential of Big Data while curtailing its potential for unethical exploitation.  

So what’s next? Extrapolating the course of the Big Data revolution is a tricky business. However, one thing is certain. Technology will continue to transform our way of life, while our social norms and our system of laws, invariably a step or two behind, will constantly struggle to adapt and stay relevant.  

by Dr. Ashok Subramanian

Dr. Ashok Subramanian is the Dean and Joel R. Stubblefield Endowed Chair in the College of Business at the University of Arkansas Fort Smith. He has both a PhD in Information Systems and an MBA from the University of Houston, as well as a BSc in Chemistry and Physics from the University of Bombay, India. He has extensive experience as a consultant and entrepreneur in the IT sector. Prior to his current position at UAFS, Dr. Subramanian’s leadership career includes being Dean of the Harold Walter Siebens School of Business at Buena Vista University, Iowa.

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